Deep Face Recognition

Deep face recognition aims to develop accurate and efficient systems for identifying individuals from facial images, focusing on overcoming challenges like variations in image quality, pose, and lighting conditions. Current research emphasizes improving model robustness through techniques like knowledge distillation, loss function modifications (e.g., angular margin losses, contrastive learning), and addressing biases related to gender and ethnicity. These advancements are crucial for enhancing the reliability and fairness of face recognition systems across diverse populations and applications, including security, law enforcement, and user authentication.